{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:J76PR5GVXKXSFKLLUTMR5FMJXP","short_pith_number":"pith:J76PR5GV","schema_version":"1.0","canonical_sha256":"4ffcf8f4d5baaf22a96ba4d91e9589bbd602d4f5b647cda9a8f3a5b7e8909075","source":{"kind":"arxiv","id":"2607.00698","version":1},"attestation_state":"computed","paper":{"title":"Quantum machine learning models for graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Frederic Rapp, Fr\\'ed\\'eric Sauvage, Mart{\\i}n Larocca, Pranav Kalidindi","submitted_at":"2026-07-01T09:45:53Z","abstract_excerpt":"Geometric Machine Learning (GML) successes have been achieved through the thorough study and design of new equivariant neural networks. In comparison, geometric quantum machine learning (GQML) models lack such a detailed understanding and, despite already several proposals, a unifying perspective on their design remains elusive. In this work, we focus on GQML models for graph problems that showcase a lot of structure and still remain frontier in machine learning. For the case when n-node graphs are encoded in n-qubit states, we provide a comprehensive characterization of their constituents. Ta"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.00698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-07-01T09:45:53Z","cross_cats_sorted":[],"title_canon_sha256":"2f07603ebbc20dc9f117cdfd95a1ccb1378e2bc64ed95841b3e9ebf8c408f0ad","abstract_canon_sha256":"8e543b4f33fd615a943e636e704ca69ac25db6f5304422f749f5d6ba9b213bee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:51.977760Z","signature_b64":"/Cn9hrSpB3dh3O0N/hjgmaxVcdx7hvmf5+uH6ooGKIZKLbrm4S/rts9gOkMJU5q/5psDPNcVA68/RXDg5ZsjBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ffcf8f4d5baaf22a96ba4d91e9589bbd602d4f5b647cda9a8f3a5b7e8909075","last_reissued_at":"2026-07-02T01:17:51.977383Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:51.977383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum machine learning models for graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Frederic Rapp, Fr\\'ed\\'eric Sauvage, Mart{\\i}n Larocca, Pranav Kalidindi","submitted_at":"2026-07-01T09:45:53Z","abstract_excerpt":"Geometric Machine Learning (GML) successes have been achieved through the thorough study and design of new equivariant neural networks. In comparison, geometric quantum machine learning (GQML) models lack such a detailed understanding and, despite already several proposals, a unifying perspective on their design remains elusive. In this work, we focus on GQML models for graph problems that showcase a lot of structure and still remain frontier in machine learning. For the case when n-node graphs are encoded in n-qubit states, we provide a comprehensive characterization of their constituents. Ta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00698","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.00698/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.00698","created_at":"2026-07-02T01:17:51.977448+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00698v1","created_at":"2026-07-02T01:17:51.977448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00698","created_at":"2026-07-02T01:17:51.977448+00:00"},{"alias_kind":"pith_short_12","alias_value":"J76PR5GVXKXS","created_at":"2026-07-02T01:17:51.977448+00:00"},{"alias_kind":"pith_short_16","alias_value":"J76PR5GVXKXSFKLL","created_at":"2026-07-02T01:17:51.977448+00:00"},{"alias_kind":"pith_short_8","alias_value":"J76PR5GV","created_at":"2026-07-02T01:17:51.977448+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP","json":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP.json","graph_json":"https://pith.science/api/pith-number/J76PR5GVXKXSFKLLUTMR5FMJXP/graph.json","events_json":"https://pith.science/api/pith-number/J76PR5GVXKXSFKLLUTMR5FMJXP/events.json","paper":"https://pith.science/paper/J76PR5GV"},"agent_actions":{"view_html":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP","download_json":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP.json","view_paper":"https://pith.science/paper/J76PR5GV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00698&json=true","fetch_graph":"https://pith.science/api/pith-number/J76PR5GVXKXSFKLLUTMR5FMJXP/graph.json","fetch_events":"https://pith.science/api/pith-number/J76PR5GVXKXSFKLLUTMR5FMJXP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP/action/storage_attestation","attest_author":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP/action/author_attestation","sign_citation":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP/action/citation_signature","submit_replication":"https://pith.science/pith/J76PR5GVXKXSFKLLUTMR5FMJXP/action/replication_record"}},"created_at":"2026-07-02T01:17:51.977448+00:00","updated_at":"2026-07-02T01:17:51.977448+00:00"}